Learning in the presence of concept drift and hidden contexts
Gerhard Widmer,Miroslav Kubat +1 more
TLDR
A family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear are described, including a heuristic that constantly monitors the system's behavior.Citations
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Learning under Concept Drift: an Overview
TL;DR: This report is intended to give a bird's view of concept drift research field, provide a context of the research and position it within broad spectrum of research fields and applications.
Journal ArticleDOI
A combinational incremental ensemble of classifiers as a technique for predicting students' performance in distance education
TL;DR: An online ensemble of classifiers that combines an incremental version of Naive Bayes, the 1-NN and the WINNOW algorithms using the voting methodology is proposed and it was found that the proposed algorithm is the most appropriate to be used for the construction of a software support tool.
Journal ArticleDOI
Discussion and review on evolving data streams and concept drift adapting
TL;DR: This survey covers different facets of existing approaches, evokes discussion and helps readers to underline the sharp criteria that allow them to properly design their own approach to concept drift handling.
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Where Should We Fix This Bug? A Two-Phase Recommendation Model
TL;DR: A two-phase prediction model that uses bug reports' contents to suggest the files likely to be fixed and compared it with three other prediction models: the Usual Suspects, the one-phase model, and BugScout to find the best prediction performance.
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PCA Feature Extraction for Change Detection in Multidimensional Unlabeled Data
TL;DR: This work proposes to apply principal component analysis (PCA) for feature extraction prior to the change detection of changes in multidimensional unlabeled data and shows that feature extraction through PCA is beneficial, specifically for data with multiple balanced classes.
References
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